Menu

Time Series
Static
50 Python scripts generated for time table this week

Time Table

Chart overview

Time tables display schedules in a structured tabular format, showing events, times, and associated details.

Key points

  • Modern Python libraries enable creation of beautifully styled schedule displays suitable for conferences, classes, or project planning.

Python Tutorial

How to create a time table in Python

Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.

How to Plot Time Series Data in Python

Example Visualization

Formatted timetable showing conference schedule

Create This Chart Now

Generate publication-ready time tables with AI in seconds. No coding required – just describe your data and let AI do the work.

View example prompt
Example AI Prompt

"Create a beautifully formatted conference schedule timetable for a 'Data Science Summit' spanning 2 days. Generate a realistic program: Day 1 has 3 tracks (AI/ML, Data Engineering, Business Intelligence) with sessions from 9:00 AM to 5:00 PM. Day 2 has keynotes and workshops. Include sessions like 'Opening Keynote: Future of AI' (9:00-10:00, Main Hall, Dr. Sarah Chen), 'Hands-on PyTorch Workshop' (10:30-12:00, Room A, 40 attendees max), 'Lunch & Networking' (12:00-1:30), etc. Format as a grid with time slots as rows, tracks as columns. Color-code by session type: Keynote (gold), Workshop (blue), Talk (green), Break (gray). Include room location and speaker name. Add capacity indicators. Title: 'Data Science Summit 2024 - Conference Schedule'."

How to create this chart in 30 seconds

1

Upload Data

Drag & drop your Excel or CSV file. Plotivy securely processes it in your browser.

2

AI Generation

Our AI analyzes your data and generates the Time Table code automatically.

3

Customize & Export

Tweak the design with natural language, then export as high-res PNG, SVG or PDF.

Newsletter

Get one weekly tip for better time tables

Join researchers receiving concise Python plotting techniques to improve chart clarity and reduce revision cycles.

No spam. Unsubscribe anytime.

Python Code Example

example.py
# === IMPORTS ===
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, FancyBboxPatch
import matplotlib.patheffects as pe

# === USER-EDITABLE PARAMETERS ===
title = "Data Science Summit 2024"
figsize = (16, 10)

# === EXAMPLE DATASET ===
schedule = [
    {'time': '9:00–10:00', 'sessions': [
        ('Opening Keynote: The Future of AI', 'Keynote', True, True, True),
    ]},
    {'time': '10:00–10:30', 'sessions': [
        ('Coffee Break & Networking', 'Break', True, True, True),
    ]},
    {'time': '10:30–12:00', 'sessions': [
        ('PyTorch Deep Dive Workshop', 'Workshop', False, False, False),
        ('Data Pipeline Architecture', 'Talk', False, False, False),
        ('Business Intelligence 101', 'Talk', False, False, False),
    ]},
    {'time': '12:00–13:30', 'sessions': [
        ('Lunch & Networking', 'Break', True, True, True),
    ]},
    {'time': '13:30–14:30', 'sessions': [
        ('LLMs in Production', 'Talk', False, False, False),
        ('Apache Spark Optimization', 'Talk', False, False, False),
        ('Tableau Masterclass', 'Workshop', False, False, False),
    ]},
    {'time': '14:30–15:30', 'sessions': [
        ('Computer Vision Advances', 'Talk', False, False, False),
        ('Real-time Data Streaming', 'Talk', False, False, False),
        ('Power BI Dashboards', 'Workshop', False, False, False),
    ]},
    {'time': '15:30–16:00', 'sessions': [
        ('Coffee Break', 'Break', True, True, True),
    ]},
    {'time': '16:00–17:00', 'sessions': [
        ('Closing Keynote: AI Ethics & Responsibility', 'Keynote', True, True, True),
    ]},
]

tracks = ['AI / ML Track', 'Data Engineering', 'Business Intelligence']

# Print summary
print("=== Conference Schedule ===")
print(f"\nTracks: {len(tracks)}")
print(f"Time Slots: {len(schedule)}")

# === CREATE TIMETABLE ===
fig, ax = plt.subplots(figsize=figsize, facecolor='#0d1117')
ax.set_facecolor('#0d1117')

# Colors
colors = {
    'Keynote': {'bg': '#6c5ce7', 'border': '#a29bfe', 'text': 'white'},
    'Workshop': {'bg': '#00b894', 'border': '#55efc4', 'text': 'white'},
    'Talk': {'bg': '#0984e3', 'border': '#74b9ff', 'text': 'white'},
    'Break': {'bg': '#2d3436', 'border': '#636e72', 'text': '#b2bec3'}
}

# Grid dimensions
cell_height = 1.0
cell_width = 2.5
time_width = 1.2
header_height = 0.6
n_rows = len(schedule)

# Draw header
for j, track in enumerate(tracks):
    x = time_width + j * cell_width
    box = FancyBboxPatch((x + 0.05, n_rows + 0.05), cell_width - 0.1, header_height - 0.1,
                          boxstyle="round,pad=0.02", facecolor='#6c5ce7', 
                          edgecolor='#a29bfe', linewidth=2)
    ax.add_patch(box)
    ax.text(x + cell_width/2, n_rows + header_height/2, track, 
            fontsize=12, fontweight='bold', color='white',
            ha='center', va='center')

# Time column header
box = FancyBboxPatch((0.05, n_rows + 0.05), time_width - 0.1, header_height - 0.1,
                      boxstyle="round,pad=0.02", facecolor='#2d3436', 
                      edgecolor='#636e72', linewidth=2)
ax.add_patch(box)
ax.text(time_width/2, n_rows + header_height/2, 'TIME', 
        fontsize=11, fontweight='bold', color='white',
        ha='center', va='center')

# Draw schedule rows
for i, row in enumerate(schedule):
    y = n_rows - i - 1
    
    # Time cell
    box = FancyBboxPatch((0.05, y + 0.05), time_width - 0.1, cell_height - 0.1,
                          boxstyle="round,pad=0.02", facecolor='#161b22', 
                          edgecolor='#30363d', linewidth=1)
    ax.add_patch(box)
    ax.text(time_width/2, y + cell_height/2, row['time'], 
            fontsize=10, fontweight='bold', color='#e6edf3',
            ha='center', va='center')
    
    sessions = row['sessions']
    
    if len(sessions) == 1 and sessions[0][2]:  # Spans all tracks
        session = sessions[0]
        style = colors[session[1]]
        x = time_width
        width = cell_width * 3
        
        box = FancyBboxPatch((x + 0.05, y + 0.05), width - 0.1, cell_height - 0.1,
                              boxstyle="round,pad=0.02", facecolor=style['bg'], 
                              edgecolor=style['border'], linewidth=2)
        ax.add_patch(box)
        
        # Session icon
        icon = '🎤' if session[1] == 'Keynote' else '☕' if session[1] == 'Break' else '💡'
        ax.text(x + width/2, y + cell_height/2 + 0.1, icon, 
                fontsize=14, ha='center', va='center')
        ax.text(x + width/2, y + cell_height/2 - 0.15, session[0], 
                fontsize=10, fontweight='bold', color=style['text'],
                ha='center', va='center', wrap=True)
    else:
        for j, session in enumerate(sessions):
            x = time_width + j * cell_width
            style = colors[session[1]]
            
            box = FancyBboxPatch((x + 0.05, y + 0.05), cell_width - 0.1, cell_height - 0.1,
                                  boxstyle="round,pad=0.02", facecolor=style['bg'], 
                                  edgecolor=style['border'], linewidth=2, alpha=0.9)
            ax.add_patch(box)
            
            # Session title (wrapped)
            title_lines = session[0].split(' ')
            if len(title_lines) > 4:
                line1 = ' '.join(title_lines[:3])
                line2 = ' '.join(title_lines[3:])
                ax.text(x + cell_width/2, y + cell_height/2 + 0.1, line1, 
                        fontsize=9, fontweight='bold', color=style['text'],
                        ha='center', va='center')
                ax.text(x + cell_width/2, y + cell_height/2 - 0.15, line2, 
                        fontsize=9, fontweight='bold', color=style['text'],
                        ha='center', va='center')
            else:
                ax.text(x + cell_width/2, y + cell_height/2, session[0], 
                        fontsize=9, fontweight='bold', color=style['text'],
                        ha='center', va='center')

# Title
ax.text(time_width + (3 * cell_width)/2, n_rows + header_height + 0.5, title,
        fontsize=24, fontweight='bold', color='white', ha='center',
        path_effects=[pe.withStroke(linewidth=3, foreground='#6c5ce7')])

# Legend
legend_y = -0.8
for idx, (session_type, style) in enumerate(colors.items()):
    x = time_width + idx * 2
    box = FancyBboxPatch((x, legend_y), 0.4, 0.3,
                          boxstyle="round,pad=0.02", facecolor=style['bg'], 
                          edgecolor=style['border'], linewidth=1)
    ax.add_patch(box)
    ax.text(x + 0.6, legend_y + 0.15, session_type, fontsize=10, color='#888',
            ha='left', va='center')

# Styling
ax.set_xlim(-0.2, time_width + 3 * cell_width + 0.5)
ax.set_ylim(-1.2, n_rows + header_height + 1)
ax.axis('off')

plt.tight_layout()
plt.savefig('chart.png', dpi=150, bbox_inches='tight', facecolor='#0d1117')
print("Saved: chart.png")
plt.show()
# END-OF-CODE

Opens the Analyze page with this code pre-loaded and ready to execute

Console Output

Output
=== Conference Schedule ===

Tracks: 3
Time Slots: 8
Saved: chart.png

Common Use Cases

  • 1Conference programs
  • 2Class schedules
  • 3Transportation timetables
  • 4Event planning

Pro Tips

Group by time or track

Use color for categories

Include duration indicators

Long-tail keyword opportunities

how to create time table in python
time table matplotlib
time table seaborn
time table plotly
time table scientific visualization
time table publication figure python

High-intent chart variations

Time Table with confidence interval overlays
Time Table optimized for publication layouts
Time Table with category-specific color encoding
Interactive Time Table for exploratory analysis

Library comparison for this chart

pandas

Good for quick exploratory drafts directly from DataFrame operations before polishing in matplotlib or plotly.

great-tables

Useful in specialized workflows that complement core Python plotting libraries for time-table analysis tasks.

plottable

Useful in specialized workflows that complement core Python plotting libraries for time-table analysis tasks.

Free Cheat Sheet

Scientific Chart Selection Cheat Sheet

Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.

Comparison Charts
Distribution Charts
Time Series Data
Common Mistakes
No spam. Unsubscribe anytime.